def __init__(self, nodes, constant_modifications=None, variable_modifications=None):
self.nodes = MassHeap(Node(s.mass, graph=self) for s in nodes)
self.edges = defaultdict(list)
self.constant_modifications = constant_modifications or []
self.variable_modifications = variable_modifications or []
self.variable_modifications += [modification.Modification("HexNAc").rule]
self.parts = MassHeap(generate_component_set(
self.constant_modifications,
self.variable_modifications))
self.long_parts = {1: self.parts}
self.node_map = {}
def generate_random_glycopeptides(target_mass, ppm_error=10e-6, count=20, constant_modifications=None,
variable_modifications=None, glycans=None, min_length=0, cleavage_start=None,
cleavage_end=None, max_missed_cleavages=1, max_glycosylations=2):
'''
Given a target mass value and a tolerance threshold around it, create a set of random glycopeptides
that satisfy the mass requirements.
'''
if glycans is None:
glycans = mammalian_glycans
if constant_modifications is None:
constant_modifications = []
else:
constant_modifications = copy.deepcopy(constant_modifications)
if variable_modifications is None:
variable_modifications = []
else:
variable_modifications = copy.deepcopy(variable_modifications)
if cleavage_start is None or len(cleavage_start) == 0:
cleavage_start = [""]
if cleavage_end is None or len(cleavage_end) == 0:
cleavage_end = [""]
cleavage_pattern = Protease(cleavage_start, cleavage_end)
variable_modifications = [
mod for mod in variable_modifications if mod.name != "HexNAc"]
constant_modifications = [
mod for mod in constant_modifications if mod.name != "HexNAc"]
components = MassHeap(map(lambda x: GrowingSequence(x, cleavage_pattern), generate_component_set(
constant_modifications, variable_modifications)))
sequons = MassHeap(
map(lambda x: GrowingSequence(x, cleavage_pattern),
itertools.chain.from_iterable(
map(lambda x: ("{0}({1}){2}".format(x[0], g.as_modification().serialize(), x[1:])
for g in glycans),
generate_n_linked_sequons()
)
)
)
)
loc_fabs = fabs
water = Composition("H2O").mass
def reset_target_mass():
return (water + target_mass) - min(p.mass for p in candidate.pad())
solutions = set()
max_iter = count * 10000
iter_count = 0
candidate = GrowingSequence("", cleavage_pattern)
mass_to_meet = reset_target_mass()
while(len(solutions) < count and iter_count < max_iter):
can_glycosylate = (len(candidate) > min_length / 3) and \
(has_glycan(candidate) < max_glycosylations) and \
(random.random() > .7)
options = list(components.get_lower_than(mass_to_meet))
if(can_glycosylate):
glycosylated_options = list(sequons.get_lower_than(mass_to_meet))
options += glycosylated_options
#logger.debug("%s options for extension, mass to meet: %s, %s" % (len(options), mass_to_meet, str(candidate)))
next_part = random.choice(options)
candidate.extend(next_part)
mass_to_meet -= (next_part.mass - water)
# print(str(candidate), candidate.missed_cleavages, len(candidate))
# Reset, too many missed cleavages?
if candidate.missed_cleavages > max_missed_cleavages:
#print("Too many missed cleavages: %s, Reset!" % candidate.missed_cleavages)
candidate = GrowingSequence("", cleavage_pattern)
mass_to_meet = reset_target_mass()
for padded_sequence in candidate.pad():
# Only consider glycosylated sequences
if has_glycan(candidate) < 1:
break
# Only consider longer sequences
if(len(padded_sequence) < min_length):
continue
error = loc_fabs(
(target_mass - padded_sequence.mass) / float(target_mass))
# logger.debug("%s, %s, %s" %
# (padded_sequence, padded_sequence.mass, error))
# Accept?
if error <= ppm_error:
#logger.debug("Accepting %s %s" %
# (padded_sequence, padded_sequence.mass))
solutions.add(str(padded_sequence))
# Reset, too big?
if mass_to_meet < components[0].mass:
candidate = GrowingSequence("", cleavage_pattern)
mass_to_meet = reset_target_mass()
iter_count += 1
return solutions
class Graph(object):
def __init__(self, nodes, constant_modifications=None, variable_modifications=None):
self.nodes = MassHeap(Node(s.mass, graph=self) for s in nodes)
self.edges = defaultdict(list)
self.constant_modifications = constant_modifications or []
self.variable_modifications = variable_modifications or []
self.variable_modifications += [modification.Modification("HexNAc").rule]
self.parts = MassHeap(generate_component_set(
self.constant_modifications,
self.variable_modifications))
self.long_parts = {1: self.parts}
self.node_map = {}
def __iter__(self):
return iter(self.nodes)
def process(self, node, parts=None):
if parts is None:
parts = self.parts
for extent in self.nodes.get_higher_than(node.mass):
gap_mass = extent.mass - node.mass
for part in parts.get_lower_than(gap_mass + precursor_mass_shift + 1):
if fabs(ppm_error(gap_mass + y_mass_shift, part.mass + y_mass_shift)) <= 2e-5:
self.edges[frozenset((node, extent))].append(Edge(
node, extent, node.mass, link_sequence=part)
)
def process_all(self, length=1):
parts = self.long_parts.get(length, None)
if parts is None:
self.build_unordered_sequences(length)
parts = self.long_parts[length]
for node in self:
self.process(node, parts)
self.build_node_map()
def build_node_map(self):
self.node_map = defaultdict(list)
for node in self:
for pair in [pair for pair in self.edges if node in pair]:
if node == min(pair, key=masser):
self.node_map[node].extend(self.edges[pair])
def roots(self):
roots = set(self.node_map)
for node, edges in self.node_map.items():
for edge in edges:
roots.discard(edge.to_terminus)
if len(edges) == 0:
roots.discard(node)
return list(roots)
def build_unordered_sequences(self, n=2):
self.long_parts[n] = MassHeap(list(unordered_combinations(self.parts, n)))
def get_sequence(self, node):
for path in self.traverse(node):
yield (node, SequenceCollection(map(lambda x: x.link_sequence, path)), path[-1].to_terminus)
def traverse(self, node):
if len(self.node_map[node]) == 0:
yield []
else:
for edge in self.node_map[node]:
for path in self.traverse(edge.to_terminus):
yield [edge] + path
def all_paths(self):
for root in self.roots():
for path in self.traverse(root):
yield (sum(map(len, path)), path)
def all_sequences(self):
for root in self.roots():
for seq in self.get_sequence(root):
yield (len(seq[1]), seq)
def identify_node(self, node, parts=None):
if parts is None:
parts = self.parts
for part in parts:
if fabs(ppm_error(node.mass, part.mass + y_mass_shift)) <= 2e-5:
node.composition.append(part)
node.kind.append('y')
elif fabs(ppm_error(node.mass, part.mass + b_mass_shift)) <= 2e-5:
node.composition.append(part)
node.kind.append('b')
return zip(node.composition, node.kind)
def sequence_spectra(ms_spectrum, drop_mass=0, constant_modifications=None, variable_modifications=None,
max_running_gaps=1, max_total_gaps=2):
constant_modifications = constant_modifications or []
variable_modifications = variable_modifications or []
variable_modifications += [modification.Modification("HexNAc").rule]
precursor_mass = ms_spectrum.neutral_mass - drop_mass
logger.info("Precursor Mass: %f", precursor_mass)
previous_sequences = SqliteDiskQueue()
parts = map(SimpleFragment,
generate_component_set(constant_modifications,
variable_modifications))
tandem = MassHeap(ms_spectrum.tandem_data)
# Get starting component
match = False
for part in parts:
for msms in tandem:
if (fabs(ppm_error(msms.neutral_mass, part.mass + y_mass_shift)) < 2e-5):
previous_sequences.append(SequenceRecord(part, kind='y'))
match = True
if (fabs(ppm_error(msms.neutral_mass, part.mass + b_mass_shift)) < 2e-5):
previous_sequences.append(SequenceRecord(part, kind='b'))
match = True
if not match:
for part in parts:
previous_sequences.append(SequenceRecord(part, 1, 1))
next_sequences = SqliteDiskQueue()
solutions = deque(maxlen=4 * max_total_gaps)
min_part = min(parts, key=lambda x: x.mass).mass
max_part = max(parts, key=lambda x: x.mass).mass
while len(previous_sequences) > 0:
for seq in previous_sequences:
match = []
lower = (seq.mass + min_part)
upper = seq.mass + max_part
lower_threshold = lower - (y_mass_shift + lower * 2e-5)
upper_threshold = upper + (y_mass_shift + upper * 2e-5)
for msms in reversed(list(tandem.get_higher_than(lower_threshold))):
if msms.neutral_mass > upper_threshold:
break
for part in parts:
mass_query = part.mass + y_mass_shift + seq.mass
if seq.kind != 'b' and (fabs(ppm_error(msms.neutral_mass, mass_query)) < 2e-5):
ext = seq.extend(part, False)
ext.matches.append(msms)
ext.kind = 'y'
next_sequences.append(ext)
match.append(msms)
# logger.info("Match on %r -> %r", ext, msms)
mass_query += -y_mass_shift + b_mass_shift
if seq.kind != 'y' and (fabs(ppm_error(msms.neutral_mass, mass_query)) < 2e-5):
ext = seq.extend(part, False)
ext.kind = 'b'
next_sequences.append(ext)
match.append(msms)
# logger.info("Match on %r -> %r", ext, msms)
if len(match) == 0:
# logger.info("No match on %r", seq)
if seq.current_gaps + 1 <= max_running_gaps and seq.total_gaps + 1 <= max_total_gaps:
for part in parts:
next_sequences.append(seq.extend(part, True))
logger.info("Round over, %d candidates", len(next_sequences))
if len(next_sequences) == 0:
return ResultsGroup([seq for round in solutions for seq in round], parts)
previous_sequences = next_sequences
solutions.append(seq for seq in next_sequences if len(seq.matches) > 0)
next_sequences = SqliteDiskQueue()